Finally, inspired by [1]}, we directly optimize the F-measure to learn the global information from groundtruth. For easy remembering, we denote F-measure as \(F_\beta \)  in the following. \(F_\beta ^{(n)}\)  is defined as:
\(precision^{(n)} = \frac{ \sum { M^{(n)} \cdot T^{(n)} }}{ \sum {M^{(n)}} + \epsilon }, \ \ recall^{(n)} = \frac{ \sum { M^{(n)} \cdot T^{(n)} }}{ \sum {T^{(n)}} + \epsilon },\) 
\(F_\beta ^{(n)} = \frac{ (1+\beta ^2) \cdot precision^{(n)} \cdot recall^{(n)} }{ \beta ^2 \cdot precision^{(n)} + recall^{(n)} },\) 
